arXiv subjects

Jian Chen

Publications and source records attributed to Jian Chen.

At least 253 records · Page 14Linked to original sources

arXiv2022

A Barzilai-Borwein Descent Method for Multiobjective Optimization Problems

The steepest descent method proposed by Fliege et al. motivates the research on descent methods for multiobjective optimization, which has received increasing attention in recent years. However, empirical results show that the Armijo line search often gives a very small stepsize along the steepest direction, which decelerates the convergence seriously. This paper points out that the issue is mainly due to the imbalances among objective functions. To address this issue, we propose a Barzilai-Borwein descent method for multiobjective optimization (BBDMO) that dynamically tunes gradient magnitudes using Barzilai-Borwein's rule in direction-finding subproblem. With monotone and nonmonotone line search techniques, it is proved that accumulation points generated by BBDMO are Pareto critical points, respectively. Furthermore, theoretical results indicate the Armijo line search can achieve a better stepsize in BBDMO. Finally, comparative results of numerical experiments are reported to illustrate the efficiency of BBDMO and verify the theoretical results.

math.OC↗

arXiv2022

On the Ohmic-dominant heating mode of capacitively-coupled plasma inverted by boundary electron emission

Electron emission from the boundary is ubiquitous in capacitively coupled plasma (CCP) and precipitates nonnegligible influences on the discharge properties. Here we present the PIC-MCC simulation of an Ohmic-dominant heating mode of capacitively coupled plasma where the stochastic heating vanishes and only Ohmic heating sustains the discharge, due to sheath inversion by boundary electron emission. The inverted CCP features negative sheath potential without Bohm presheath, hence excluding plasma heating due to sheath edge oscillation. The particle and energy transport of the proposed heating mode is analyzed. The influences of boundary electron emission flux, source voltage, and neutral pressure on the transition between classic and Ohmic-dominant CCP heating modes are shown with designated simulation scans. A modified inverse sheath-plasma coupling due to excessive ionization is discovered. In the end, key indicators of the proposed heating mode in plasma diagnostics are provided for future experimental verifications.

physics.plasm-ph↗

arXiv2022

Electron transport in the single-layer semiconductor

Two-dimensional (2D) materials are a new class of materials with interesting physical properties and applications ranging from nanoelectronics to sensing and photonics. In addition to graphene, the most studied 2D material, monolayers of other layered materials such as semiconducting dichalcogenides MoS2 or WSe2 are gaining in importance as promising channel materials for field-effect transistors (FETs) and phototransistors. However, it is unclear that how the specific process of electron transport is affected by temperature. So, nowadays the electron dynamics of single-layer semiconductor cannot be understood fundamentally. Here, we develop an analytical theory distinguishing from traditional energy band theory, backed up by Monte-Carlo simulations, that predicts the process of electron transport and the effect of temperature on the electron transport in the single-layer semiconductor. In this paper, A new model is built to deal with electron transporting in the sing-layer semiconductor. The resistance is decided by the barrier rather than the electron scattering in the single-layer semiconductor, which is macroscopic quantum effect. Electron transport in FETs with different dielectric configurations are investigated at different temperatures and a new control factor that is decided by top-gate voltage or bottom-gate voltage is introduced to describe the effect of gate voltage on the electron transport in 2D semiconductor. The results of simulation show the drain current is mainly determined by some elements, such as temperature, top-gate voltage, bottom-gate voltage and source-drain voltage.

cond-mat.mtrl-sci↗

arXiv2022

Charge transport in monolayers of metal nanoparticles

Two-dimensional (2D) nanoparticle films are a new class of materials with interesting physical properties and applications ranging from nanoelectronics to sensing and photonics. The importance of conducting nanoparticle films makes the fundamental understanding of their charge transport extremely important for materials and process design. Various hopping and transport mechanisms have been proposed and the nanoparticle monolayer is consistent with the electrical equivalent RC circuit, but their theoretical methods are limited to the model of the single electron tunneling between capacitively coupled nanoparticles with a characteristic time constant RC and the conductivity of thin film is the experimental conductivity, which cannot be deduced from these theoretical models. It is also unclear that how the specific process of electron transpot is affected by temperature. So, nowadays the electron dynamics of thin film cannot be understood fundamentally. Here, we develop an analytical theory based on the model of Sommerfeld, backed up by Monte-Carlo simulations, that predicts the process of charge transport and the effect of temperature on the electron transport in the thin film. In this paper two different nanoparticle models were built to cope with different types of morphology: triangular array and rectangular array. The transport properties of these different kinds of arrays including 2D ordered nanoparticle arrays with/without local structural disorder and 2D gradient nanoparticle arrays were investigated at different temperatures. For 2D well-ordered nanoparticle array without local structural disorder, the I-V curves are non-linear and highly symmetric.

cond-mat.mtrl-sci↗

arXiv2022

New Massive Contact Twin Binary in a Radio-quiet HII Region Associated with the M17 Complex

Early-B stars may create an HII region that appears as radio-quiet. We report the identification of new early-B stars associated with the radio-quiet HII region G014.645--00.606 in the M17 complex. The ratio-quiet HII region G014.645--00.606 is adjacent to three radio-quiet WISE HII region candidates. The ionizing sources of the radio-quiet HII regions are expected to later than B1V, given the sensitivity about 1-2 mJy of the MAGPIS 20 cm survey. The stars were first selected if their parallaxes of GAIA EDR3 match that of the 22 GHz H$_2$O maser source within the same region. We used the color-magnitude diagram made from the ZTF photometric catalog to select the candidates for massive stars because the intrinsic $g-r$ colors of massive stars change little from B-type to O-type stars. Five stars lie in the areas of the color-magnitude diagram where either reddened massive stars or evolved post-main sequence stars of lower masses are commonly found. Three of the five stars, sources 1, 2, and 3, are located at the cavities of the three IR bubbles, and extended H$α$ emission is detected around the three IR bubbles. We suggest that sources 1, 2, and 3 are candidates for early-B stars associated with the radio-quiet region G014.645--00.606. Particularly, source 1 is an EW type eclipsing binary with a short period of 0.825 day, while source 2 is an EA type eclipsing binary with a short period of 0.919 day. The physical parameters of the two binary systems have been derived through the PHOEBE model. Source 1 is a twin binary of two stars with T~23,500 K, and source 2 contains a hotter component (T~20,100 K) and a cooler one (T~15,500 K). The $O-C$ values of source 1 show a trend of decline, implying that the period of the source is deceasing. Source 1 is likely a contacting early-B twin binary, for which mass transfer might cause its orbit to shrink.

astro-ph.SR↗

arXiv2022

Towards optical toroidal wavepacket through tightly focusing of cylindrical vector two dimensional spatiotemporal optical vortex

Spatiotemporal optical vortices (STOVs) carrying transverse orbital angular momentum (OAM) are of rapidly growing interest for the field of optics due to the new degree of freedom that can be exploited. In this paper, we propose cylindrical vector two dimensional STOVs (2D-STOVs) containing two orthogonal transverse OAMs in both x-t and y-t planes for the first time, and investigate the tightly focusing of such fields using the Richards-Wolf vectorial diffraction theory. Highly confined spatiotemporal wavepackets with polarization structure akin to toroidal topology is generated, whose spatiotemporal intensity distributions resemble the shape of Yo-Yo balls. Highly focused radially polarized 2D-STOVs will produce wavepackets towards transverse magnetic toroidal topology, while the focused azimuthally polarized 2D-STOVs will give rise to wavepackets towards transverse electric toroidal topology. The presented method may pave a way to experimentally generate the optical toroidal wavepackets in a controllable way, with potential applications in electron acceleration, photonics, energy, transient light-matter interaction, spectroscopy, quantum information processing, etc.

physics.optics↗

arXiv2022

Giant viscoelasticity near Mott criticality in PbCrO3 with large lattice anomalies

Coupling of charge and lattice degrees of freedom in materials can produce intriguing electronic phenomena, such as conventional superconductivity where the electrons are mediated by lattice for creating supercurrent. The Mott transition, which is a source for many fascinating emergent behaviors, is originally thought to be driven solely by correlated electrons with an Ising criticality. Recent studies on the known Mott systems have shown that the lattice degree of freedom is also at play, giving rise to either Landau or unconventional criticality. However, the underlying coupling mechanism of charge and lattice degrees of freedom around the Mott critical endpoint remains elusive, leading to difficulties in understanding the associated Mott physics. Here we report a study of Mott transition in cubic PbCrO3 by measuring the lattice parameter, using high-pressure x-ray diffraction techniques. The Mott criticality in this material is revealed with large lattice anomalies, which is governed by giant viscoelasticity that presumably results from a combination of lattice elasticity and electron viscosity. Because of the viscoelastic effect, the lattice of this material behaves peculiarly near the critical endpoint, inconsistent with any existing university classes. We argue that the viscoelasticity may play as a hidden degree of freedom behind the Mott criticality.

cond-mat.str-el↗

arXiv2022

Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers,i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

eess.SY↗

arXiv2021

Forex Trading Volatility Prediction using Neural Network Models

In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques. We show step-by-step how to construct the deep-learning network by the guidance of the empirical patterns of the intra-day volatility. The numerical results show that the multiscale Long Short-Term Memory (LSTM) model with the input of multi-currency pairs consistently achieves the state-of-the-art accuracy compared with both the conventional baselines, i.e. autoregressive and GARCH model, and the other deep learning models.

q-fin.ST↗

arXiv2021

Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation

Image manipulation with StyleGAN has been an increasing concern in recent years.Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images.However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i.e., local attribute translation.To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles. Specifically, we collaboratively manipulate the modulation style channels and feature maps in control units rather than individual ones to obtain the semantic and spatial disentangled controls. Furthermore, we propose a simple yet effective method to detect the attribute-specific control units. We move the modulation style along a specific sparse direction vector and replace the filter-wise styles used to compute the feature maps to manipulate these control units. We evaluate our proposed method in various face attribute manipulation tasks. Extensive qualitative and quantitative results demonstrate that our proposed method performs favorably against the state-of-the-art methods. The manipulation results of real images further show the effectiveness of our method.

cs.CV↗

arXiv2021

A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy

Many real-world optimization problems such as engineering design can be eventually modeled as the corresponding multiobjective optimization problems (MOPs) which must be solved to obtain approximate Pareto optimal fronts. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been regarded as a significantly promising approach for solving MOPs. Recent studies have shown that MOEA/D with uniform weight vectors is well-suited to MOPs with regular Pareto optimal fronts, but its performance in terms of diversity usually deteriorates when solving MOPs with irregular Pareto optimal fronts. In this way, the solution set obtained by the algorithm can not provide more reasonable choices for decision makers. In order to efficiently overcome this drawback, we propose an improved MOEA/D algorithm by virtue of the well-known Pascoletti-Serafini scalarization method and a new strategy of multi-reference points. Specifically, this strategy consists of the setting and adaptation of reference points generated by the techniques of equidistant partition and projection. For performance assessment, the proposed algorithm is compared with existing four state-of-the-art multiobjective evolutionary algorithms on benchmark test problems with various types of Pareto optimal fronts. According to the experimental results, the proposed algorithm exhibits better diversity performance than that of the other compared algorithms. Finally, our algorithm is applied to two real-world MOPs in engineering optimization successfully.

cs.NE↗

arXiv2021

On the Secrecy Design of STAR-RIS assisted Uplink NOMA Networks

This paper investigates the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access system, where the legitimate users send confidential signals to the base station by exploiting STAR-RIS to reconfigure the electromagnetic propagation environment proactively. Depending on the availability of the eavesdropping channel state information (CSI), both the full CSI and statistical CSI of the eavesdropper are considered. For the full eavesdropping CSI scenario, we adopt the adaptive-rate wiretap code scheme with the aim of maximizing minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. To proceed, we propose an alternating hybrid beamforming (AHB) algorithm to jointly optimize the receive beamforming, transmit power, and reflection/transmission coefficients. While for the statistical eavesdropping CSI scenario, the constant-rate wiretap code scheme is employed to minimize the maximum secrecy outage probability (SOP) subject to the quality-of-service requirements of legitimate users. Then, we derive the exact SOP expression under the constant-rate coding strategy and develop an extended AHB algorithm for the joint secrecy beamforming design. Simulation results demonstrate the effectiveness of the proposed scheme. Moreover, some useful guidance about the quantification of phase shift/amplitude and the deployment of STAR-RIS is provided.

cs.IT↗

arXiv2021

Doping evolution of superconductivity, charge order and band topology in hole-doped topological kagome superconductors Cs(V$_{1-x}$Ti$_x$)$_3$Sb$_5$

The newly discovered Kagome superconductors $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) exhibit superconductivity, charge order, and band topology simultaneously. To explore the intricate interplay between the superconducting and charge orders, we investigate the doping evolution of superconductivity, charge-density-wave (CDW) order, and band topology in doped topological kagome superconductors Cs(V$_{1-x}$Ti$_x$)$_3$Sb$_5$ where the Ti-dopant introduces hole-like charge carriers. Despite the absence of the CDW phase transition in doped compounds even for the lowest doping level of $x=0.047$, the superconductivity survives in all doped samples with enhanced critical temperatures. The high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements reveal that the Ti-dopant in the kagome plane lowers the chemical potential, pushing the van Hove singularity (VHS) at $M$ point above the Fermi level. First-principle simulations corroborate the doping evolution of the band structure observed in ARPES, and affirm that the CDW instability does not occur once the VHS is pushed above the Fermi level, explaining the absence of the CDW ordering in our doped samples Cs(V$_{1-x}$Ti$_x$)$_3$Sb$_5$. Our results demonstrate a competition between the CDW and superconducting orders in the kagome-metal superconductor CsV$_3$Sb$_5$, although the superconductivity is likely inconsequential of the CDW order.

cond-mat.supr-con↗

arXiv2021

Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

cs.LG↗

arXiv2021

Adaptive Multi-layer Contrastive Graph Neural Networks

We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of sample data without data labels. AMC-GNN generates two graph views by data augmentation and compares different layers' output embeddings of Graph Neural Network encoders to obtain feature representations, which could be used for downstream tasks. AMC-GNN could learn the importance weights of embeddings in different layers adaptively through the attention mechanism, and an auxiliary encoder is introduced to train graph contrastive encoders better. The accuracy is improved by maximizing the representation's consistency of positive pairs in the early layers and the final embedding space. Our experiments show that the results can be consistently improved by using the AMC-GNN framework, across four established graph benchmarks: Cora, Citeseer, Pubmed, DBLP citation network datasets, as well as four newly proposed datasets: Co-author-CS, Co-author-Physics, Amazon-Computers, Amazon-Photo.

cs.LG↗

arXiv2021

Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM

A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed. To enhance the discernibility of geomagnetic signals, the time-series preprocessing approach is constructed at first. Next, the TCN is invoked to expand the feature dimensions on the basis of keeping the time-series characteristics of LSTM model. Then, a multi-scale time-series layer is constructed with multiple TCNs of different dilation factors to address the problem of inconsistent time-series speed between localization model and mobile users. A stacking framework of multi-scale TCN and LSTM is eventually proposed for indoor magnetic localization. Experiment results demonstrate the effectiveness of the proposed algorithm in indoor localization.

eess.SP↗

arXiv2021

Photonic toroidal vortex

Toroidal vortices are whirling disturbances rotating about a ring-shaped core while advancing in the direction normal to the ring orifice. Toroidal vortices are commonly found in nature and being studied in a wide range of disciplines. Here we report the experimental observation of photonic toroidal vortex as a new solution to Maxwell's equations with the use of conformal mapping. The helical phase twists around a closed loop leading to an azimuthal local orbital angular momentum density. The preparation of such intriguing light field may offer insights of extending toroidal vortex to other disciplines and find important applications in light-matter interaction, optical manipulation, photonic symmetry and topology, and quantum information.

physics.optics↗

arXiv2021

Improve photon number discrimination for a superconducting series nanowire detector by applying a digital matched filter

Photon number resolving (PNR) is an important capacity for detectors working in quantum and classical applications. Although a conventional superconducting nanowire single-photon detector (SNSPD) is not a PNR detector, by arranging nanowires in a series array and multiplexing photons over space, such series PNR-SNSPD can gain quasi-PNR capacity. However, the accuracy and maximum resolved photon number are both limited by the signal-to-noise (SNR) ratio of the output pulses. Here, we introduce a matched filter, which is an optimal filter in terms of SNR for SNSPD pulses. Experimentally, compared to conventional readout using a room-temperature amplifier, the normalized spacing between pulse amplitudes from adjacent photon number detections increased by a maximum factor of 2.1 after the matched filter. Combining with a cryogenic amplifier to increase SNR further, such spacing increased by a maximum factor of 5.3. In contrast to a low pass filter, the matched filter gave better SNRs while maintaining good timing jitters. Minimum timing jitter of 55 ps was obtained experimentally. Our results suggest that the matched filter is a useful tool for improving the performance of the series PNR-SNSPD and the maximum resolved photon number can be expected to reach 65 or even large.

quant-ph↗